Wireless Compressive Sensing for Energy Harvesting Sensor Nodes
Wireless Compressive Sensing for Energy Harvesting Sensor Nodes
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DOI：
10.1109/tsp.2013.2271480
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发表时间：
2012-11
影响因子：
5.4
通讯作者：
Gang Yang;V. Tan;Chin Keong Ho;S. Ting;Y. Guan
中科院分区：
文献类型：
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作者：
Gang Yang;V. Tan;Chin Keong Ho;S. Ting;Y. Guan
我们考虑的情况下，多个传感器发送空间相关的数据到融合中心（FC）通过独立的瑞利衰落信道加性噪声。假设传感器数据是稀疏的，在某些基础上，我们表明，这种稀疏信号的恢复可以制定为一个压缩感知（CS）问题。为了模拟传感器使用从环境中收获的间歇性可用能量进行操作的场景，我们建议每个传感器以一定的概率独立发送，并将发送功率适应其收获的能量。由于概率传输，等效传感矩阵的元素不是高斯的。此外，由于传感器具有不同的能量收集速率和不同的传感器到FC的距离，因此FC对于每个传感器具有不同的接收信噪比（SNR）。这被称为SNR的不均匀性。因此，感测矩阵的元素也不是相同分布的。对于这种非常规的设置，我们提供了可靠的和计算效率的恢复测量的数量上的理论保证，通过显示的传感矩阵满足限制等距属性（RIP），在合理的条件下。然后，我们计算一个可实现的系统延迟下允许的均方误差（MSE）。此外，使用大偏差理论的技术，我们分析了所谓的k-限制的特征值，这决定了RIP举行所需的测量次数的SNR的不均匀性的影响。我们的结论是，RIP所需的测量的数量是不敏感的SNR的不均匀性，当传感器的数量n是大的和稀疏的传感器数据（信号）k的增长速度慢于n的平方根。我们的分析是由大量的数值结果证实。
We consider the scenario in which multiple sensors send spatially correlated data to a fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of this sparse signal can be formulated as a compressive sensing (CS) problem. To model the scenario in which the sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor. This is referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. For this unconventional setting, we provide theoretical guarantees on the number of measurements for reliable and computationally efficient recovery, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under reasonable conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. We conclude that the number of measurements required for the RIP is not sensitive to the inhomogeneity of SNRs, when the number of sensors n is large and the sparsity of the sensor data (signal) k grows slower than the square root of n. Our analysis is corroborated by extensive numerical results.